Matplotlib
Pandas
NumPy
Seaborn
D3.js
Plotly
GnuPlot
Jupyter
DevDock
Docker Desktop
DevDock keeps local projects in one sidebar and gives each project a focused workspace for its overview, commands, run history, databases, security checks, settings, and tools. The Today view surfaces recent projects and saved daily workflows. Inside a project, DevDock connects registered folders, detected technologies, Docker and Git state, database operations, local security findings, and the actions used to get back to work.
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DevDock's answer:
DevDock brings local software projects, saved commands, Docker environments, database operations, project health, and security checks into one Windows desktop workspace. Each project has a focused view for its overview, commands, run history, databases, security checks, settings, and tools, while the Today view surfaces recent projects and saved daily workflows.
DevDock's answer:
DevDock is a fit for developers who switch between local codebases and want repeatable project context in one place. It connects registered folders, detected technologies, saved commands, Git and Docker state, database operations, local security findings, and project health checks without requiring repositories to be moved into one folder or uploaded to a service.
DevDock's answer:
DevDock is primarily for Windows developers who switch between local codebases, work across frontend, backend, mobile, and infrastructure repositories, or want repeatable local setup and project workflows without uploading source code.
Based on our record, Matplotlib seems to be more popular. It has been mentiond 114 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
In February, an AI agent named MJ Rathbun submitted a pull request to matplotlib โ the Python plotting library used by half the scientific computing world. Scott Shambaugh, a volunteer maintainer, rejected it. Standard code review. Nothing unusual. - Source: dev.to / 5 months ago
Numbers are useful, but sometimes itโs easier to spot patterns when you can actually see your data. Pandas works seamlessly with Matplotlib, a popular Python library for creating visualizations. Together, they make it easy to turn raw numbers into clear charts. - Source: dev.to / 9 months ago
We are storing the results in JSON files, which we combine, analyze and visualize using matplotlib in Python. Here's the structure of a benchmark result file:. - Source: dev.to / 9 months ago
NetworkX and Matplotlib were used to visualize the graph structure of the agent. - Source: dev.to / 10 months ago
The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโฆ. - Source: dev.to / 11 months ago
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Docker Desktop - Docker Desktop is a one-click-install application that lets you to build, share, and run containerized applications and microservices.
NumPy - NumPy is the fundamental package for scientific computing with Python
Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.
D3.js - D3.js is a JavaScript library for manipulating documents based on data. D3 helps you bring data to life using HTML, SVG, and CSS.
Plotly - Low-Code Data Apps